Gradient Boosting: How to Predict Customer Churn

Gradient boosting is a machine learning method widely used in marketing. It has gained popularity for its ability to handle complex data relationships. In marketing, gradient boosting is applied to better predict customer behavior, identify the most effective promotion channels, and optimize campaigns.

What is gradient boosting?

Gradient boosting is a technique for solving complex tasks like price prediction, customer behavior analysis, or risk assessment. Simply put, it’s a method that trains a machine to gradually learn from its mistakes until it produces accurate results.

Imagine trying to guess how many steps it takes to walk to work. At first, you estimate 1,000 steps. Halfway there, you realize you’re off—you’ve already taken 800 steps, and you’re far from your destination. So, you recalculate and add another 500 steps. Next time, you use previous errors to refine your prediction, considering route length, walking speed, and other factors.

Gradient boosting works similarly: it creates simple models, each improving upon the errors of the last, and iteratively becomes more accurate.

Another key aspect is its use of decision trees. A decision tree acts like a roadmap, helping the machine divide data into groups to identify patterns. Gradient boosting builds many such trees, with each new tree added to correct the shortcomings of the earlier ones. Together, they function as a team, achieving high accuracy.

Put simply, at each step, the model adjusts its focus to better correct errors. Here’s how it works:

• If changing the prediction for a particular example significantly reduces the overall error, the model pays more attention to that example. It tries to predict a value that minimizes the error as much as possible.

• If changing the prediction has little impact on the error, the model decides that the example isn’t worth prioritizing and won’t overly adjust to it.

The name “gradient boosting” comes from the fact that the model sets its goals based on the gradient of the error — that is, how the error changes depending on the prediction. At each step, the model takes a small, precise step toward improvement until its predictions are as accurate as possible.

This method is particularly useful for handling complex or incomplete data. In marketing, for instance, gradient boosting helps predict which customers are more likely to purchase a product, which promotions will be most effective, or even identify why customers are “churning.” This makes it a powerful tool for analysts.

What is churn rate?

Churn rate, also known as the customer attrition rate, helps a company understand how many people stopped using its services over a specific period. This rate is typically expressed as a percentage. For instance, if the churn rate for a month is 20%, it means that one out of every five profiles became inactive during that time.

This metric is essential because it indicates how frequently users leave your brand. High churn negatively impacts a business’s revenue for a simple reason: acquiring new customers is more expensive than retaining existing ones.

Churn rate is a key marketing metric for assessing a business’s health, especially in industries like subscription services or mobile apps. A high churn rate may signal low product quality, insufficient customer support, or strong competition in the market.

Customer loss also sheds light on weaknesses within a business. For example, if users leave the platform en masse after a particular stage, it might point to issues with the onboarding process or hidden difficulties in using the product. Therefore, analyzing churn rate allows businesses to make strategic decisions to improve the customer experience.

If too many people are "churning," it’s time to focus on boosting the retention rate. To learn more about improving retention, read this article: Retention Rate: How to Calculate and Improve the Metric.

What is considered a normal churn rate?

A normal churn rate depends on the industry and type of business. For instance, an 11% churn rate is considered acceptable for IT companies, while in logistics, it could be around 40%. It’s essential to compare your rate with competitors in the same niche.

That said, the primary goal for any company is to aim for a lower churn rate. Even a small reduction of 1–2% can lead to significant profit increases by retaining existing customers. Ultimately, a normal churn rate is one that doesn’t hinder business growth or profitability.

How gradient boosting helps predict churn rate

In today’s environment, customer loyalty should not be viewed as constant. The abundance of alternatives makes it likely even for regular users to switch to competitors. This is why it’s crucial to identify at-risk customers in advance and take action to prevent their departure.

Predictive models not only determine the overall churn rate but also track how it changes over time depending on customer groups, product lines, or other factors. This data provides valuable insights for decision-making. However, since customers differ in their preferences and behaviors, standard approaches often fall short.

This is where machine learning methods like gradient boosting come into play. Predictive models based on gradient boosting allow for deeper data analysis and more accurate churn prediction, making them highly effective in identifying potential customer attrition.

How it works

Gradient boosting combines several simple models (decision trees) into one powerful system. Each new tree is designed to correct the errors of the previous one, gradually improving overall accuracy.

Below is a step-by-step explanation of how this process works.

Step 1. Start with the simplest model.

Imagine we want to predict which customers are likely to churn. Initially, the algorithm builds the simplest decision tree. This tree makes rough predictions, such as: “Customers with expensive subscriptions are more likely to stay.”

Step 2. Identify the errors.

Next, the model checks where it made mistakes. For instance, it might incorrectly predict that a low-spending customer would churn when they actually stayed. These errors are recorded.

Step 3. Add a new tree.

A second tree is built based on the errors of the first one. It is specifically designed to address those mistakes. For example, the model might learn: “Not all customers on lower-tier plans churn; the length of their subscription also matters.”

Step 4. Repeat the process.

This process is repeated multiple times. Each new tree improves the predictions by focusing on correcting the errors of the previous ones.

Step 5. Combine everything together.

All the trees work together: each tree handles a specific part of the task, and collectively they produce an accurate prediction.

Why is it effective?

• Gradient boosting can handle various types of data, including numbers, text, and categories.

• The model learns from its mistakes step by step, which makes it increasingly accurate over time.

• It also allows you to identify which factors (e.g., subscription type or payment amount) are most significant.

Imagine you have customer data for a company:

• Customer 1: Monthly subscription, paid $50 → churned.

• Customer 2: Annual subscription, paid $70 → retained.

• Customer 3: Monthly subscription, paid $30 → churned.

The model first identifies a simple pattern: customers with annual subscriptions are more likely to stay. Then, it notices that among monthly subscribers, those paying lower amounts tend to churn. Step by step, the model refines its predictions and becomes more accurate.

How does this work in practice?

When you run gradient boosting:

• You split your data into training and testing sets.

• The gradient boosting model trains on the training data to identify patterns.

• Then, you test it on the testing data to ensure it works correctly.

• Afterward, you use it to make predictions and identify customers at risk of churning, so you can reach out to them in time.

This method is especially effective for complex tasks with large datasets and multiple factors influencing the outcome.

How accurate is it?

With gradient boosting, you can achieve quite high accuracy. Compared to simpler models, which might have an accuracy of around 70-73%, gradient boosting improves this figure to 80% or higher. This is because the method focuses on correcting errors made in previous steps, progressively making more accurate predictions.

Of course, the accuracy depends on the quality of the data. The more information you have about customers (their behavior, preferences, and history), the more precise the predictions will be. However, even with a limited dataset, gradient boosting can deliver solid results by identifying key factors that influence a customer’s decision to stay or leave.

Ultimately, gradient boosting not only predicts who might churn but also uncovers the main characteristics and patterns that most significantly impact churn rates. This provides businesses with actionable insights to minimize customer loss and improve loyalty.

Conclusion

Gradient boosting is a powerful tool for accurately predicting customer behavior, including their likelihood to churn. By leveraging this approach, companies can not only increase the accuracy of their forecasts but also gain a deeper understanding of the factors that influence a customer’s decision to stay or leave.

This opens up new opportunities for targeting and retention strategies, enabling businesses to make more informed decisions and allocate resources more effectively. In an environment of high competition and extensive data, gradient boosting becomes an indispensable tool for optimizing marketing strategies.